Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
Section 1: Introduction to Machine Learning
Section 2: Data Engineering and Exploratory Data Analysis
Section 3: Data Modeling


First, you were introduced to the different types of features that you might have to work with. Identifying the type of variable you'll be working with is very important for defining the types of transformations and techniques that can be applied to each case.

Then, we learned how to deal with categorical features. We saw that, sometimes, categorical variables do have an order (such as the ordinal ones), while other times, they don't (such as the nominal ones). You learned that one-hot encoding (or dummy variables) is probably the most common type of transformation for nominal features; however, depending on the number of unique categories, after applying one-hot encoding, your data might suffer from sparsity issues. Regarding ordinal features, you shouldn't create dummy variables on top of them, since you would be losing the information of order that's been incorporated into the variable. In those cases, ordinal encoding is the most appropriate transformation...